class_docstr: Base video anomalib dataset class.
  
  Args:
      task (str): Task type, either 'classification' or 'segmentation'
      transform (A.Compose): Albumentations Compose object describing the transforms that are applied to the inputs.
      clip_length_in_frames (int): Number of video frames in each clip.
      frames_between_clips (int): Number of frames between each consecutive video clip.
      target_frame (VideoTargetFrame): Specifies the target frame in the video clip, used for ground truth retrieval
docstr: Return mask, clip and file system information.
docstr_tok: ["Return","mask",",","clip","and","file","system","information","."]
code: def __getitem__(self, index: int) -> dict[str, str | Tensor]:
      """Return mask, clip and file system information."""
      assert isinstance(self.indexer, ClipsIndexer)
  
      item = self.indexer.get_item(index)
      # include the untransformed image for visualization
      item["original_image"] = item["image"].to(torch.uint8)
  
      # apply transforms
      if "mask" in item and item["mask"] is not None:
          processed_frames = [
              self.transform(image=frame.numpy(), mask=mask)
              for frame, mask in zip(item["image"], item["mask"])
          ]
          item["image"] = torch.stack(
              [item["image"] for item in processed_frames]
          ).squeeze(0)
          mask = torch.as_tensor(item["mask"])
          item["mask"] = torch.stack(
              [item["mask"] for item in processed_frames]
          ).squeeze(0)
          item["label"] = (
              Tensor([1 in frame for frame in mask]).int().squeeze(0)
          )
          if self.task == TaskType.DETECTION:
              item["boxes"], _ = masks_to_boxes(item["mask"])
              item["boxes"] = (
                  item["boxes"][0]
                  if len(item["boxes"]) == 1
                  else item["boxes"]
              )
      else:
          item["image"] = torch.stack(
              [
                  self.transform(image=frame.numpy())["image"]
                  for frame in item["image"]
              ]
          ).squeeze(0)
  
      # include only target frame in gt
      if (
          self.clip_length_in_frames > 1
          and self.target_frame != VideoTargetFrame.ALL
      ):
          item = self._select_targets(item)
  
      if item["mask"] is None:
          item.pop("mask")
  
      return item
  
imports: from __future__ import annotations
  from abc import ABC
  from enum import Enum
  from typing import Callable
  import albumentations
  import torch
  from pandas import DataFrame
  from torch import Tensor
  from anomalib.data.base.datamodule import AnomalibDataModule
  from anomalib.data.base.dataset import AnomalibDataset
  from anomalib.data.task_type import TaskType
  from anomalib.data.utils import ValSplitMode, masks_to_boxes
  from anomalib.data.utils.video import ClipsIndexer
label_desc: Use image node_id 7 for calling the AnomalibVideoDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: item